calculate.NGSk: Calculate NGSk (NTk-like) statistics with gene label...
In sigPathway: Pathway Analysis

Description Usage Arguments Details Value Author(s) References Examples

Calculates the NGSk (NTk-like) statistics with gene label permutation and the corresponding p-values and q-values for each selected pathway.

1 2	calculate.NGSk(statV, gsList, nsim = 1000, verbose = FALSE, alwaysUseRandomPerm = FALSE)

`statV`	a numeric vector of test statistic (not p-values) for each individual probe/gene
`gsList`	a list containing three vectors from the output of the `selectGeneSets` function
`nsim`	an integer indicating the number of permutations to use
`verbose`	a boolean to indicate whether to print debugging messages to the R console
`alwaysUseRandomPerm`	a boolean to indicate whether the algorithm can use complete permutations for cases where `nsim` is greater than the total number of unique permutations possible with the `phenotype` vector

This function is a generalized version of NTk calculations; calculate.NTk calls this function internally. To use this function, the user must specify a vector of test statistics (e.g., t-statistic, Wilcoxon). Pathways from this function can be ranked with rankPathways.NGSk or with rankPathways when combined with results from another pathway analysis algorithm (e.g., calculate.NEk).

A list containing

`ngs`	number of gene sets
`nsim`	number of permutations performed
`t.set`	a numeric vector of Tk/Ek statistics
`t.set.new`	a numeric vector of NTk/NEk statistics
`p.null`	the proportion of nulls
`p.value`	a numeric vector of p-values
`q.value`	a numeric vector of q-values

Lu Tian, Peter Park, and Weil Lai

Tian L., Greenberg S.A., Kong S.W., Altschuler J., Kohane I.S., Park P.J. (2005) Discovering statistically significant pathways in expression profiling studies. Proceedings of the National Academy of Sciences of the USA, 102, 13544-9.

http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102

## Load in filtered, expression data
data(MuscleExample)

## Prepare the pathways to analyze
probeID <- rownames(tab)
gsList <- selectGeneSets(G, probeID, 20, 500)

nsim <- 1000
ngroups <- 2
verbose <- TRUE
weightType <- "constant"
methodName <- "NGSk"
npath <- 25
allpathways <- FALSE
annotpkg <- "hgu133a.db"

statV <- calcTStatFast(tab, phenotype, ngroups)$tstat
res.NGSk <- calculate.NGSk(statV, gsList, nsim, verbose)

## Summarize top pathways from NGSk
res.pathways.NGSk <-
  rankPathways.NGSk(res.NGSk, G, gsList, methodName, npath)
print(res.pathways.NGSk)

## Get more information about the probe sets' means and other statistics
## for the top pathway in res.pathways.NGSk
gpsList <-
  getPathwayStatistics.NGSk(statV, probeID, G, res.pathways.NGSk$IndexG,
                            FALSE, annotpkg)
print(gpsList[[1]])

## Write table of top-ranked pathways and their associated probe sets to
## HTML files
parameterList <-
  list(nprobes = nrow(tab), nsamples = ncol(tab),
       phenotype = phenotype, ngroups = ngroups,
       minNPS = 20, maxNPS = 500, ngs = res.NGSk$ngs,
       nsim.NGSk = res.NGSk$nsim,
       annotpkg = annotpkg, npath = npath, allpathways = allpathways)

writeSP(res.pathways.NGSk, gpsList, parameterList, tempdir(),
        "sigPathway_cNGSk", "TopPathwaysTable.html")